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Try executing this chunk by clicking the Run button within the chunk or by placing your cursor inside it and pressing Ctrl+Shift+Enter. # First part ## data filter and normalization

source("./tianfengRwrappers.R")

载入需要的程辑包：dplyr

载入程辑包：‘dplyr’

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NOTE: Either Arial Narrow or Roboto Condensed fonts are required to use these themes.
      Please use hrbrthemes::import_roboto_condensed() to install Roboto Condensed and
      if Arial Narrow is not on your system, please see https://bit.ly/arialnarrow

Registered S3 method overwritten by 'enrichplot':
  method               from
  fortify.enrichResult DOSE
clusterProfiler v3.14.3  For help: https://guangchuangyu.github.io/software/clusterProfiler

If you use clusterProfiler in published research, please cite:
Guangchuang Yu, Li-Gen Wang, Yanyan Han, Qing-Yu He. clusterProfiler: an R package for comparing biological themes among gene clusters. OMICS: A Journal of Integrative Biology. 2012, 16(5):284-287.

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========================================
circlize version 0.4.13
CRAN page: https://cran.r-project.org/package=circlize
Github page: https://github.com/jokergoo/circlize
Documentation: https://jokergoo.github.io/circlize_book/book/

If you use it in published research, please cite:
Gu, Z. circlize implements and enhances circular visualization
  in R. Bioinformatics 2014.

This message can be suppressed by:
  suppressPackageStartupMessages(library(circlize))
========================================

载入需要的程辑包：grid
========================================
ComplexHeatmap version 2.2.0
Bioconductor page: http://bioconductor.org/packages/ComplexHeatmap/
Github page: https://github.com/jokergoo/ComplexHeatmap
Documentation: http://jokergoo.github.io/ComplexHeatmap-reference

If you use it in published research, please cite:
Gu, Z. Complex heatmaps reveal patterns and correlations in multidimensional 
  genomic data. Bioinformatics 2016.
========================================


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冠状动脉

dataset from Nat. Med.

human_coronary_countmatrix <- read.csv("GSE131778_human_coronary_scRNAseq.txt", sep = "\t")
func <- function(s) {
  paste0(strsplit(s, ".", fixed = T)[[1]][2], "_", strsplit(s, ".", fixed = T)[[1]][1])
}
colnames(human_coronary_countmatrix) <- lapply(colnames(human_coronary_countmatrix), func) # 拆分样本

human_coronary <- CreateSeuratObject(counts = human_coronary_countmatrix, 
                                     project = "human_coronary", min.cells = 10, min.features = 300) %>% 
    PercentageFeatureSet(pattern = "^MT-", col.name = "percent.mt") %>%
    subset(subset = nFeature_RNA > 600 & nFeature_RNA < 6000 & nCount_RNA > 1000 &  nCount_RNA < 30000) %>%
    SCTransform(vars.to.regress = "percent.mt", verbose = F) %>% 
    RunPCA() %>% FindNeighbors(dims = 1:20) %>% 
    RunUMAP(dims = 1:20) %>% 
    FindClusters(resolution = 0.1)
rm(human_coronary_countmatrix)

颈动脉斑块 CA dataset1

# 批量读取计数矩阵
# 需要把行名的gene删掉，用vscode修改
count_mats <- list.files("./CA_GSE155512")
count_mats <- count_mats[count_mats != "sampleinfo.txt"]
allList <- lapply(count_mats, function(folder) {
  CreateSeuratObject(
    counts = read.csv(paste0("./CA_GSE155512/", folder), sep = "\t"),
    project = folder, min.cells = 10, min.features = 300
  )
})
# 合并seurat对象
CA_dataset1 <- merge(allList[[1]],
  y = allList[-1], add.cell.ids = count_mats,
  project = "CA_dataset1"
)
rm(allList)

CA_dataset1 <- PercentageFeatureSet(CA_dataset1, pattern = "^MT-", col.name = "percent.mt") %>%
    subset(subset = nFeature_RNA > 600 & nFeature_RNA < 6000 & nCount_RNA > 1000 &  nCount_RNA < 30000) %>%
    SCTransform(vars.to.regress = "percent.mt", verbose = F) %>% 
    RunPCA() %>% FindNeighbors(dims = 1:20) %>% 
    RunUMAP(dims = 1:20) %>% 
    FindClusters(resolution = 0.1)

颈动脉斑块 CA dataset2

CA_dataset2 <- CreateSeuratObject(Read10X("./CA_GSE159677/"), names.field = 2, names.delim = "-",
                                   project = "CA_dataset2", min.cells = 10, min.features = 300) %>% 
  PercentageFeatureSet(pattern = "^MT-", col.name = "percent.mt") %>%
  subset(subset = nFeature_RNA > 600 & nFeature_RNA < 6000 & nCount_RNA > 1000 &  nCount_RNA < 30000) %>%
  SCTransform(vars.to.regress = "percent.mt", verbose = F) %>% 
  RunPCA() %>% FindNeighbors(dims = 1:20) %>% 
  RunUMAP(dims = 1:20) %>% 
  FindClusters(resolution = 0.1)

添加metadata samples存储完整信息，conditions按区域分，groups按病例分

Idents(human_coronary) <- human_coronary$orig.ident
Idents(human_coronary) <- c("1","1","2","2","3","3","4","4")
human_coronary$samples <- Idents(human_coronary)
Idents(human_coronary) <- human_coronary$seurat_clusters

Idents(CA_dataset2) <- CA_dataset2$orig.ident
CA_dataset2 <- RenameIdents(CA_dataset2,'1' = 'AC_1','2' = 'PA_1','3' = 'AC_2','4' = 'PA_2','5' = 'AC_3','6' = 'PA_3')
UMAPPlot(CA_dataset2)

CA_dataset2$sample <- Idents(CA_dataset2)
CA_dataset2 <- RenameIdents(CA_dataset2,'AC_1' = 'AC','PA_1' = 'PA','AC_2'= 'AC','PA_2'= 'PA','AC_3'= 'AC','PA_3'= 'PA')
CA_dataset2$conditions <- Idents(CA_dataset2)
Idents(CA_dataset2) <- CA_dataset2$orig.ident
CA_dataset2 <- RenameIdents(CA_dataset2, '1' = 'sp_1','2' = 'sp_1','3' = 'sp_2','4' = 'sp_2','5' = 'sp_3','6' = 'sp_3')
CA_dataset2$groups <- Idents(CA_dataset2)
Idents(CA_dataset2) <- CA_dataset2$seurat_clusters

保存结果

saveRDS(human_coronary,"human_coronary.rds")
saveRDS(CA_dataset1,"CA_dataset1.rds")
saveRDS(CA_dataset2,"CA_dataset2.rds") #已经经过分组处理了

Idents(CA_dataset2) <- CA_dataset2$conditions
AC <- subset(CA_dataset2, idents = "AC")
PA <- subset(CA_dataset2, idents = "PA")

ds2_PA <- ds2_PA %>% RunPCA() %>% FindNeighbors(dims = 1:20) %>% 
  RunUMAP(dims = 1:20, seed.use = 20) %>% 
  FindClusters(resolution = 0.1)

PC_ 1 
Positive:  CCL4, HLA-DRA, CD74, HLA-DRB1, HLA-DPA1, CCL5, SPP1, CCL4L2, LYZ, FTL 
       CCL3, FTH1, B2M, HLA-DPB1, C1QB, C1QA, TYROBP, FABP5, CD69, RNASE1 
       C1QC, HLA-DQA1, ACKR1, NKG7, CXCR4, AREG, HLA-DRB5, IL7R, SRGN, GZMK 
Negative:  TAGLN, ACTA2, MGP, MYL9, IGFBP7, TPM2, CALD1, RGS5, MYH11, TPM1 
       C11orf96, BGN, SPARCL1, CTGF, LUM, DSTN, PPP1R14A, IGFBP2, OGN, PLN 
       ADIRF, FHL1, COL14A1, SOD3, NOV, LMOD1, CYR61, MYH10, AEBP1, COL1A2 
PC_ 2 
Positive:  ACTA2, MYL9, TAGLN, TPM2, MYH11, RGS5, PLN, C11orf96, CALD1, PPP1R14A 
       LMOD1, DSTN, MFAP4, CNN1, RAMP1, MYH10, ITGA8, C12orf75, TPM1, CSRP2 
       FILIP1L, ACTG2, CSRP1, SYNPO2, NEXN, FLNA, MYLK, MCAM, PPP1R12B, ACTC1 
Negative:  MGP, LUM, CFH, TIMP1, COL1A2, COL3A1, BGN, DCN, SFRP2, COL1A1 
       IGFBP7, CTGF, APOE, POSTN, FN1, AEBP1, CRTAC1, VCAN, C1R, NDUFA4L2 
       PCOLCE, COL6A3, THY1, CYR61, CCDC80, TNC, CCL19, GAP43, IGFBP3, PRSS23 
PC_ 3 
Positive:  COL3A1, LUM, COL1A1, SPARCL1, DCN, THY1, COL6A3, COL1A2, MYH11, RGS5 
       POSTN, C11orf96, IBSP, APOE, TIMP3, TNC, PLN, SERPINF1, CALD1, COL6A1 
       FN1, CCL19, CPE, TIMP1, PCOLCE, COL6A2, MMP11, SFRP2, LMOD1, SPARC 
Negative:  MGP, CTGF, IGFBP2, IGFBP7, TNFRSF11B, FRZB, EFEMP1, OGN, CLU, NOV 
       IGFBP6, ACTC1, SUCNR1, ASPN, SOST, SFRP5, CRYAB, IGFBP3, COL8A1, OMD 
       PTN, DLX6-AS1, C2orf40, RAMP1, CYTL1, ID3, SGCG, GSN, BGN, PDE5A 
PC_ 4 
Positive:  FN1, LUM, DCN, COL1A2, CRTAC1, COL3A1, MYH11, COL1A1, CLU, VCAN 
       MYH10, POSTN, IGFBP6, OGN, IBSP, TNFRSF11B, PRSS23, NOV, AEBP1, SLPI 
       SFRP4, PLN, LTBP1, MGP, COL8A1, HTRA1, DPT, PPP1R14A, ASPN, FAP 
Negative:  APOE, CCL19, IGFBP7, RGS5, NDUFA4L2, STEAP4, CCL21, CCDC102B, IGFBP5, C2orf40 
       COX4I2, FRZB, C7, RGS16, AGT, PGF, ID4, CYR61, PLAC9, SEPT4 
       HIGD1B, RARRES2, TDO2, LHFPL6, SOD3, NR2F2, ANGPT2, GGT5, THY1, MT2A 
PC_ 5 
Positive:  SFRP2, C2orf40, TAGLN, CTGF, CYR61, CCL19, DCN, DPT, FRZB, IGFBP2 
       IGFBP7, PTGDS, RARRES2, CP, FMO2, FGF7, ACTC1, COL6A3, SERPINF1, CXCL12 
       TIMP3, THY1, TNC, COL1A1, SLPI, SFRP4, LUM, SOD3, CCL21, RAMP1 
Negative:  APOE, SPARCL1, MGP, TFPI2, RGS5, CFH, NDUFA4L2, GSN, MYH11, AGT 
       IGFBP3, PCOLCE2, SULF1, OGN, COL18A1, VCAN, ADH1B, DKK3, COL6A2, ASPN 
       APCDD1, COLEC11, BGN, DIO2, CRLF1, LTBP1, CRHBP, LHFPL6, EFEMP1, COL14A1 
Computing nearest neighbor graph
Computing SNN
16:41:40 UMAP embedding parameters a = 0.9922 b = 1.112
16:41:40 Read 6498 rows and found 20 numeric columns
16:41:40 Using Annoy for neighbor search, n_neighbors = 30
16:41:40 Building Annoy index with metric = cosine, n_trees = 50
0%   10   20   30   40   50   60   70   80   90   100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
16:41:41 Writing NN index file to temp file /tmp/Rtmpptkzpe/file3f09af16ba45
16:41:41 Searching Annoy index using 1 thread, search_k = 3000
16:41:42 Annoy recall = 100%
16:41:43 Commencing smooth kNN distance calibration using 1 thread
16:41:44 Initializing from normalized Laplacian + noise
16:41:44 Commencing optimization for 500 epochs, with 273294 positive edges
0%   10   20   30   40   50   60   70   80   90   100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
16:41:50 Optimization finished

Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck

Number of nodes: 6498
Number of edges: 224605

Running Louvain algorithm...

0%   10   20   30   40   50   60   70   80   90   100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|

Maximum modularity in 10 random starts: 0.9344
Number of communities: 4
Elapsed time: 0 seconds

umapplot(ds2_AC, group.by = "Classification1")

Scale for 'colour' is already present. Adding another scale for 'colour', which will replace the
existing scale.

umapplot(ds2_PA, group.by = "Classification1")

Scale for 'colour' is already present. Adding another scale for 'colour', which will replace the
existing scale.

saveRDS(ds2_AC,"ds2_AC.rds")
saveRDS(ds2_PA,"ds2_PA.rds")